Show simple item record

dc.contributor.advisorRivera Rodriguez, Sergio Raul
dc.creatorBarón Moreno, Carlos Eduardo
dc.date.accessioned2020-06-11T22:50:37Z
dc.date.available2020-06-11T22:50:37Z
dc.date.created2019-12-13
dc.identifier.urihttps://repositorio.unal.edu.co/handle/unal/77650
dc.descriptionPlanear la programación de la operación de una microred mediante algoritmos de optimización metaheurísticos permite que las microredes sean más eficientes. Esto se debe a que una microred se puede manejar como un sistema de potencia que tiene que operar conjuntamente y que tiene varios aspectos como generación renovable, generación convencional, almacenamiento de energía y control de cargas. El test bed o la red de prueba utilizada en el desarrollo del presente trabajo consta de generación solar, dos generadores eólicos, un sistema de almacenamiento en baterías, dos sisteamas que gestionan vehículos eléctricos. Este trabajo tiene tres objetivos. El primero es formular una ecuación que describa el costo de operar una microred. El segundo es encontrar el punto de operación tal que el costo asociado a la operación de la microred se obtenga a través del algoritmo metaheurístico con nombre DEEPSO que por sus siglas en ingles significa Differential Evolutionary Particle Swarm Optimization, donde este valor es el mínimo. Finalmente, el tercer objetivo es analizar cómo la programación más adecuada para la reducción de los costos es afectada por la vida útil del almacenamiento de energía eléctrica. Después del desarrollo del trabajo, se analizó y encontró que en un horizonte de tiempo de 24 horas el uso de almacenamiento de energía permite que existan ahorros o incluso ganancias solamente en el uso de la microred. Así mismo, se observó que al transcurrir el tiempo y el sistema de almacenamiento envejece, este se acerca cada vez más a los sistemas actuales, que tienen un régimen de operación no gestionable (es decir que la inyección solar y eólica no se puede controlar). Estos sistemas tienen como ideal despachar toda la energía renovable disponible en ese mismo instante, pero sin planear el despacho de forma inteligente, por ejemplo, usando excedentes de energía almacenados en baterías en otros momentos de tiempo
dc.description.abstractPlanning the operation scheduling of the microgrid by using optimization heuristic algorithms allows the microgrids to be more efficient. This is possible since a microgrid can work as a power system that has to operate jointly having different aspects namely renewable and traditional power generation, energy storage and controllable loads. The test bed used during the development of this study consists of two aggregators that manage electric vehicles, power generation, photovoltaic, battery bank and two wind turbine generators. This research has three objectives: the first is to formulate an equation that describes the operation cost of the microgrid; the second aims to find such an operating point that the operation cost of the microgrid can be obtained through the DEEPSO (Differential Evolutionary Particle Swarm Optimization), metaheuristic algorithm, in which this value is minimum. Finally, the third objective is to analyze how the most adequate programming for the reduction of costs is affected by the useful life of the electric energy storage. After the development of this research, it was found that in a 24-hour horizon time, the use of energy storage allows the existence of savings or even profits only by using the microgrid, actually. Likewise, it was observed that as the time passes and the storage system gets old, the mentioned system resembles more to the current systems that have a no manageable operating regime (it means that the wind and solar power feed cannot be controlled). Ideally, these systems dispatch all the available renewable energy during that specific time, but without having planned a smart dispatch, for instance, by using the energy surplus stored in the batteries in other periods of time.
dc.format.extent113
dc.format.mimetypeapplication/pdf
dc.language.isospa
dc.rights.urihttp://creativecommons.org/publicdomain/zero/1.0/
dc.subjectAlgoritmos Metaheurísticos
dc.subjectAlmacenamiento de Energía
dc.subjectCosto de Incertidumbre
dc.subjectDEEPSO
dc.subjectDespacho Económico
dc.subjectEnergías Renovables
dc.subjectMicrored
dc.subjectVehículos Eléctricos
dc.subjectVida Útil
dc.subject.ddc530 - Física::537 - Electricidad y electrónica
dc.titleProgramación de la operación horaria de una microred minimizando el costo de operación usando el algoritmo heurístico DEEPSO
dc.typeOther
dc.rights.spaAcceso abierto
dc.contributor.institutionUniversidad Nacional de Colombia - Sede Bogotá
dc.subject.keywordMetaheuristic Algorithm
dc.subject.keywordUseful Life.
dc.subject.keywordElectric Vehicles
dc.subject.keywordMicrogrid
dc.subject.keywordRenewable Energies
dc.subject.keywordEconomic Dispatch
dc.subject.keywordDEEPSO,
dc.subject.keywordUncertainty Cost
dc.subject.keywordEnergy Storage
dc.type.spaOtro
dc.type.hasversionAccepted Version
dc.contributor.gruplacGrupo de Investigación EMC-UN
dc.coverage.modalityMaestria
dc.rights.accessRightsOpen Access
dc.rights.ccCC0 1.0 Universal
dc.identifier.bibliographicCitation[1] “The Paris Agreement | UNFCCC.” .
dc.identifier.bibliographicCitation[2] M. L. H. y E. M. E. 2016 García Arbeláez, C., G. Vallejo, El acuerdo de parís así actuará Colombia frente al cambio climático. .
dc.identifier.bibliographicCitation[3] “Ministerio de Minas y Energía.” .
dc.identifier.bibliographicCitation[4] “Unidad de Planeación Minero Energética UPME.” .
dc.identifier.bibliographicCitation[5] “Comisión de Regulación de Energía y Gas - CREG.” .
dc.identifier.bibliographicCitation[6] “Superintendencia de Servicios Públicos Domiciliarios.” .
dc.identifier.bibliographicCitation[7] “Superintendencia de Industria y Comercio.” .
dc.identifier.bibliographicCitation[8] “Sistema de Información Eléctrico Colombiano SIMEC.” .
dc.identifier.bibliographicCitation[9] R. C. Siabato Benavides, “Identificación de proyectos con potencial de generación de energía eólica como complemento a otras fuentes de generación eléctrica en el departamento de Boyacá,” p. 156, 2018.
dc.identifier.bibliographicCitation[10] S. Surender Reddy, J. Y. Park, and C. M. Jung, “Optimal operation of microgrid using hybrid differential evolution and harmony search algorithm,” Front. Energy, vol. 10, no. 3, pp. 355–362, 2016.
dc.identifier.bibliographicCitation[11] J. N. Bharothu, M. Sridhar, and R. S. Rao, “Modified adaptive differential evolution based optimal operation and security of AC-DC microgrid systems,” Int. J. Electr. Power Energy Syst., vol. 103, pp. 185–202, Dec. 2018.
dc.identifier.bibliographicCitation[12] T. K. Kristoffersen, K. Capion, and P. Meibom, “Optimal charging of electric drive vehicles in a market environment,” Appl. Energy, 2011.
dc.identifier.bibliographicCitation[13] H. L. Li, X. M. Bai, and W. Tan, “Impacts of plug-in hybrid electric vehicles charging on distribution grid and smart charging,” in 2012 IEEE International Conference on Power System Technology, POWERCON 2012, 2012.
dc.identifier.bibliographicCitation[14] J. Zhao, F. Wen, Z. Yang Dong, S. Member, Y. Xue, and K. Po Wong, “Optimal Dispatch of Electric Vehicles and Wind Power Using Enhanced Particle Swarm Optimization,” IEEE Trans. Ind. INFORMATICS, vol. 8, no. 4, 2012.
dc.identifier.bibliographicCitation[15] H. Kamankesh, V. G. Agelidis, and A. Kavousi-Fard, “Optimal scheduling of renewable micro-grids considering plug-in hybrid electric vehicle charging demand,” Energy, 2016.
dc.identifier.bibliographicCitation[16] Z. Ma, D. S. Callaway, and I. A. Hiskens, “Decentralized Charging Control of Large Populations of Plug-in Electric Vehicles,” IEEE Trans. Control Syst. Technol., vol. 21, no. 1, 2013.
dc.identifier.bibliographicCitation[17] A. Sheikhi, S. Bahrami, A. M. Ranjbar, and H. Oraee, “Strategic charging method for plugged in hybrid electric vehicles in smart grids; A game theoretic approach,” Int. J. Electr. Power Energy Syst., 2013.
dc.identifier.bibliographicCitation[18] R. H. Lasseter, “MicroGrids,” in 2002 IEEE Power Engineering Society Winter Meeting. Conference Proceedings (Cat. No.02CH37309), 2002, vol. 1, pp. 305–308 vol.1.
dc.identifier.bibliographicCitation[19] N. Salvaterra, “Las baterias gigantescas alimentan los planes para las energías eólicas y solar,” p. B 8, 2019.
dc.identifier.bibliographicCitation[20] B. Zhao, X. Zhang, J. Chen, C. Wang, and L. Guo, “Operation optimization of standalone microgrids considering lifetime characteristics of battery energy storage system,” IEEE Trans. Sustain. Energy, vol. 4, no. 4, pp. 934–943, 2013.
dc.identifier.bibliographicCitation[21] C. Baron and S. Rivera, “Mono-objective minimization of operation cost for a microgrid with renewable power generation, energy storage and electric vehicles,” Rev. Int. Métodos Numéricos para Cálculo y Diseño en Ing., 2019.
dc.identifier.bibliographicCitation[22] M. Ross, R. Hidalgo, C. Abbey, and G. Joós, “Energy storage system scheduling for an isolated microgrid,” IET Renew. Power Gener., 2011.
dc.identifier.bibliographicCitation[23] H. Morais, P. Kádár, P. Faria, Z. A. Vale, and H. M. Khodr, “Optimal scheduling of a renewable micro-grid in an isolated load area using mixed-integer linear programming,” Renew. Energy, 2010.
dc.identifier.bibliographicCitation[24] Y. A. Katsigiannis, P. S. Georgilakis, and E. S. Karapidakis, “Multiobjective genetic algorithm solution to the optimum economic and environmental performance problem of small autonomous hybrid power systems with renewables,” IET Renew. Power Gener., 2010.
dc.identifier.bibliographicCitation[25] R. Dufo-López and J. L. Bernal-Agustín, “Multi-objective design of PV-wind-diesel-hydrogen-battery systems,” Renew. Energy, 2008.
dc.identifier.bibliographicCitation[26] S. X. Chen and H. B. Gooi, “Jump and shift method for multi-objective optimization,” IEEE Trans. Ind. Electron., 2011.
dc.identifier.bibliographicCitation[27] C. Chen, S. Duan, T. Cai, B. Liu, and G. Hu, “Smart energy management system for optimal microgrid economic operation,” IET Renew. Power Gener., 2011.
dc.identifier.bibliographicCitation[28] J. Li, F. Liu, Z. Wang, S. H. Low, and S. Mei, “Optimal Power Flow in Stand-alone DC Microgrids,” IEEE Trans. Power Syst., vol. 33, no. 5, pp. 5496–5506, 2017.
dc.identifier.bibliographicCitation[29] IEEE PES Working Group on Modern Heuristic Optimization, “Competition on ‘Application of Modern Heuristic Optimization Algorithms for Solving Optimal Power Flow Problems,’” 2014.
dc.identifier.bibliographicCitation[30] J. Arévalo, F. Santos, and S. Rivera, “Application of Analytical Uncertainty Costs of Solar, Wind and Electric Vehicles in Optimal Power Dispatch,” Ingeniería, vol. 22, no. 3, pp. 324–346, 2017.
dc.identifier.bibliographicCitation[31] S. Rivera, C. Arevalo, and F. Santos, “Uncertainty Cost Functions for Solar Photovoltaic Generation, Wind Energy Generation, and Plug-In Electric Vehicles: Mathematical Expected Value and Verification by Monte Carlo Simulation,” Int. J. Power Energy Convers., vol. 10, no. 2, pp. 171–207, 2018.
dc.identifier.bibliographicCitation[32] D. U. Sauer and H. Wenzl, “Comparison of different approaches for lifetime prediction of electrochemical systems-Using lead-acid batteries as example,” J. Power Sources, 2008.
dc.identifier.bibliographicCitation[33] C. Liu, X. Wang, X. Wu, and J. Guo, “Economic scheduling model of microgrid considering the lifetime of batteries,” IET Gener. Transm. Distrib., vol. 11, no. 3, pp. 759–767, 2017.
dc.identifier.bibliographicCitation[34] F. Katiraei, R. Iravani, N. Hatziargyriou, and A. Dimeas, “Microgrids Management,” IEEE Trans. Smart Grid, no. june, pp. 54–65, 2008.
dc.identifier.bibliographicCitation[35] F. Garcia-torres and C. Bordons, “Optimal Economical Schedule of Hydrogen-Based Microgrids With Hybrid Storage Using Model Predictive Control,” IEEE Trans. Ind. Electron., vol. 62, no. 8, pp. 5195–5207, 2015.
dc.identifier.bibliographicCitation[36] A. Arabali, M. Ghofrani, M. S. Fadali, and Y. Baghzouz, “Genetic-Algorithm-Based Optimization Approach for Energy Management,” IEEE Trans. Power Deliv., vol. 28, no. 1, pp. 162–170, 2013.
dc.identifier.bibliographicCitation[37] S. Bahramirad, W. Reder, and A. Khodaei, “Reliability-Constrained Optimal Sizing of Energy Storage System in a Microgrid,” IEEE Trans. Smart Grid, vol. 3, no. 4, pp. 2056–2062, 2012.
dc.identifier.bibliographicCitation[38] G. Carpinelli, F. Mottola, D. Proto, and A. Russo, “A Multi-Objective Approach for Microgrid Scheduling,” IEEE Trans. Smart Grid, vol. 8, no. 5, pp. 2109–2118, 2017.
dc.identifier.bibliographicCitation[39] S. Teleke et al., “Control Strategies for Battery Energy Storage for Wind Farm Dispatching,” IEEE Trans. Energy Convers., vol. 24, no. 3, pp. 725–732, 2009.
dc.identifier.bibliographicCitation[40] P. Mercier, R. Cherkaoui, S. Member, and A. Oudalov, “Optimizing a Battery Energy Storage System for Frequency Control Application in an Isolated Power System,” ieee tra, vol. 24, no. 3, pp. 1469–1477, 2009.
dc.identifier.bibliographicCitation[41] R. A. Gallego Rendon, A. H. Escobar Zuluaga, and E. M. Toro Ocampo, Tecnicas metaheuristicas de optimizacion., Segunda. Pereira: Universidad Tecnológica de Pereira, 2008.
dc.identifier.bibliographicCitation[42] J. L. Acosta, A. E. Alarcon, and S. Rivera, “Reconfiguración de sistemas de distribución para minimizar pérdidas utilizando optimización heurística: Métodos BPSO y DEEPSO,” Entre Cienc. e Ing., vol. 11, no. 22, pp. 110–117, Oct. 2017.
dc.identifier.bibliographicCitation[43] L. Dkul et al., “Unit Commitment With Non-Smooth Generation Cost Function Using Binary Particle Swarm Optimization,” Int. Semin. Intell. technoology its Appl., pp. 5–10, 2016.
dc.identifier.bibliographicCitation[44] C. A. Hernandez-Aramburo, T. C. Green, and N. Mugniot, “Fuel consumption minimization of a microgrid,” IEEE Trans. Ind. Appl., 2005.
dc.identifier.bibliographicCitation[45] M. Alramlawi, E. Mohagheghi, and P. Li, “Predictive active-reactive optimal power dispatch in PV-battery-diesel microgrid considering reactive power and battery lifetime costs,” Sol. Energy, vol. 193, no. September, pp. 529–544, 2019.
dc.identifier.bibliographicCitation[46] D. Soto, “Modeling and measurement of specific fuel consumption in diesel microgrids in Papua, Indonesia,” Energy Sustain. Dev., vol. 45, pp. 180–185, 2018.
dc.identifier.bibliographicCitation[47] M. I. Ennes and A. L. Diniz, “for Economic Dispatch Problems,” Computing, pp. 1–6, 2012.
dc.identifier.bibliographicCitation[48] N. Martinez et al., “Computer model for a wind–diesel hybrid system with compressed air energy storage,” Energies, vol. 12, no. 18, pp. 1–18, 2019.
dc.identifier.bibliographicCitation[49] Q. Zhang, Z. Ren, R. Ma, M. Tang, and Z. He, “Research on Double-Layer Optimized Configuration of Multi-Energy Storage in Regional Integrated Energy System with Connected Distributed Wind Power,” Energies, vol. 12, no. 3964, pp. 1–16, 2019.
dc.identifier.bibliographicCitation[50] “China Renewable Energy Outlook 2018.”
dc.identifier.bibliographicCitation[51] “Ofgem - Making a positive difference for energy consumers.” .
dc.identifier.bibliographicCitation[52] “Microgeneration Certification Scheme (MCS): Small installations | Ofgem.” .
dc.identifier.bibliographicCitation[53] “MCS - Tthe Microgeneration certification scheme.” .
dc.identifier.bibliographicCitation[54] Z. Xu, Z. Hu, Y. Song, W. Zhao, and Y. Zhang, “Coordination of PEVs charging across multiple aggregators,” Appl. Energy, 2014.
dc.identifier.bibliographicCitation[55] A. Hussain, V. Bui, J. Baek, and H. Kim, “Stationary Energy Storage System for Fast EV Charging Stations : Simultaneous Sizing of Battery,” Energies, vol. 12, no. 4516, 2019.
dc.identifier.bibliographicCitation[56] A. Serpi and M. Porru, “Modelling and Design of Real-Time Energy Management Systems for Fuel Cell / Battery Electric Vehicles,” Energies, vol. 12, no. 4260, 2019.
dc.identifier.bibliographicCitation[57] S. Z. Frida Berglund and M. K. and K. Uhlen, “Optimal Operation of Battery Storage for a Subscribed Capacity-Based Power Tariff,” Energies, vol. 12, no. 4450, 2019.
dc.identifier.bibliographicCitation[58] T. Sikorsk et al., “A Case Study on Distributed Energy Resources and Energy-Storage Systems in a Virtual Power Plant Concept : Economic Aspects,” Energies, vol. 12, no. 4447, 2019.
dc.identifier.bibliographicCitation[59] A. Castellazzi, E. Gurpinar, Z. Wang, A. S. Hussein, and P. G. Fernandez, “Impact of Wide-Bandgap Technology on Renewable Energy and Smart-Grid Power Conversion Applications Including Storage,” Energies, vol. 12, no. 4462, 2019.
dc.identifier.bibliographicCitation[60] C. Jankowiak, A. Zacharopoulos, C. Brandoni, P. Keatley, P. MacArtain, and N. Hewitt, “The role of domestic integrated battery energy storage systems for electricity network performance enhancement,” Energies, vol. 12, no. 3954, pp. 1–27, 2019.
dc.identifier.bibliographicCitation[61] A. Chaouachi, R. M. Kamel, R. Andoulsi, and K. Nagasaka, “Multiobjective Intelligent Energy Management for a Microgrid _ Aymen Chaouachi - Academia,” IEEE Trans. Ind. Electron., vol. 60, no. 4, pp. 1688–1699, 2013.
dc.identifier.bibliographicCitation[62] U.S. Energy Information Administration (EIA), “Annual Energy Outlook 2013 with projections to 2040.” Washington, DC, p. 244, 2013.
dc.identifier.bibliographicCitation[63] Planeamiento Minero-Energético (UPME), “Integración de las energías renovables no convencionales en Colombia,” Minist. Minas y Energía, pp. 23–33, 2015.
dc.identifier.bibliographicCitation[64] “GWEC | GLOBAL WIND REPORT 2018,” 2019.
dc.identifier.bibliographicCitation[65] B. K. Sahu, “Wind energy developments and policies in China: A short review,” Renew. Sustain. Energy Rev., vol. 81, pp. 1393–1405, 2018.
dc.identifier.bibliographicCitation[66] J. Garcia-guarin et al., “Smart Microgrids Operation Considering a Variable Neighborhood Search : The Differential Evolutionary Particle Swarm Optimization Algorithm,” Energies, vol. 12, no. 3149, pp. 1–13, 2019.
dc.contributor.generoMasculino
dc.publisher.programBogotá - Ingeniería - Maestría en Ingeniería - Ingeniería Eléctrica


Files in this item

Thumbnail
Thumbnail

This item appears in the following Collection(s)

Show simple item record

http://creativecommons.org/publicdomain/zero/1.0/This work is licensed under a Creative Commons Reconocimiento-NoComercial 4.0.This document has been deposited by the author (s) under the following certificate of deposit